ImVoteNet：融合图像与点云的3D物体检测提升

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ImVoteNet是一种针对RGB-D场景的3D对象检测算法，由Charles R. Qi、Xinlei Chen等人提出，他们隶属于Facebook AI和斯坦福大学的研究团队。在深度学习驱动的3D点云对象检测领域取得了显著进展，但点云数据存在天然的局限性，如稀疏性、缺乏颜色信息以及易受传感器噪声影响。相比之下，图像具有高分辨率和丰富的纹理信息，可以弥补3D几何结构的不足。 ImVoteNet在VOTENET这一先进点云对象检测模型的基础上进行扩展，其核心在于融合2D图像中的投票信息（2D votes）与3D点云中的投票信息（3D votes）。它不同于先前的多模态检测方法，ImVoteNet特别注重从2D图像中提取几何和语义特征，这通过利用相机参数将2D特征精确地转换到三维空间中。这种融合策略旨在充分利用图像的视觉信息来增强点云的检测性能，尤其是在识别和定位目标时。该工作通过以下步骤提升3D检测效果： 1. **特征提取**：从2D图像中提取丰富的几何和语义特征，这些特征包含了物体的形状、纹理和颜色信息。 2. **2D-3D融合**：通过相机参数将2D特征映射到3D空间，与点云中的3D信息相结合，形成一个更全面的特征表示。 3. **投票机制**：结合2D和3D的投票信息，可能包括候选区域的置信度、边界框或关键点位置，以提高目标检测的准确性和鲁棒性。 4. **检测网络**：设计一个专门针对RGB-D场景的3D检测网络，能够整合来自不同模态的数据，优化检测算法的整体性能。 ImVoteNet的优势在于它能够有效融合图像和点云的优势，减少单一模态的局限性，从而在复杂的现实环境中实现更精确、更全面的3D对象检测。由于它强调了图像信息在3D检测中的重要作用，这项研究对于那些依赖于多源数据融合的智能机器人、自动驾驶汽车以及增强现实等领域有着潜在的实际应用价值。

ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes

Charles R. Qi

∗†

Xinlei Chen

∗1

Or Litany

1,2

Leonidas J. Guibas

1,2

Facebook AI

Stanford University

Abstract

3D object detection has seen quick progress thanks to

advances in deep learning on point clouds. A few recent

works have even shown state-of-the-art performance with

just point clouds input (e.g. VOTENET ). However, point

cloud data have inherent limitations. They are sparse, lack

color information and often suffer from sensor noise. Im-

ages, on the other hand, have high resolution and rich tex-

ture. Thus they can complement the 3D geometry provided

by point clouds. Yet how to effectively use image informa-

tion to assist point cloud based detection is still an open

question. In this work, we build on top of VOTENET and

propose a 3D detection architecture called IMVOTENET

specialized for RGB-D scenes. IMVOTENET is based on

fusing 2D votes in images and 3D votes in point clouds.

Compared to prior work on multi-modal detection, we ex-

plicitly extract both geometric and semantic features from

the 2D images. We leverage camera parameters to lift these

features to 3D. To improve the synergy of 2D-3D feature

fusion, we also propose a multi-tower training scheme. We

validate our model on the challenging SUN RGB-D dataset,

advancing state-of-the-art results by 5.7 mAP. We also pro-

vide rich ablation studies to analyze the contribution of

each design choice.

1. Introduction

Recognition and localization of objects in a 3D envi-

ronment is an important ﬁrst step towards full scene un-

derstanding. Even such low dimensional scene represen-

tation can serve applications like autonomous navigation

and augmented reality. Recently, with advances in deep

networks for point cloud data, several works [

33, 56, 41]

have shown state-of-the-art 3D detection results with point

cloud as the only input. Among them, the recently proposed

VOTENET [

33] work by Qi et al., taking 3D geometry in-

put only, showed remarkable improvement for indoor ob-

ject recognition compared with previous works that exploit

*: equal contributions.

†: work done while at Facebook.

Figure 1. Voting using both an image and a point cloud from

an indoor scene. The 2D vote reduces the search space of the 3D

object center to a ray while the color texture in image provides a

strong semantic prior. Motivated by the observation, our model

lifts the 2D vote to 3D to boost 3D detection performance.

all RGB-D channels. This leads to an interesting research

question: Is 3D geometry data (point clouds) sufﬁcient for

3D detection, or is there any way RGB images can further

boost current detectors?

By examining the properties of point cloud data and RGB

image data (see for example Fig.

1), we believe the answer

is clear: RGB images have value in 3D object detection. In

fact, images and point clouds provide complementary in-

formation. RGB images have higher resolution than depth

images or LiDAR point clouds and contain rich textures

that are not available in the point domain. Additionally,

images can cover “blind regions” of active depth sensors

which often occur due to reﬂective surfaces. On the other

hand, images are limited in the 3D detection task as they

lack absolute measures of object depth and scale, which are

exactly what 3D point clouds can provide. These observa-

tions, strengthen our intuition that images can help point

cloud-based 3D detection.

However, how to make effective use of 2D images in a

3D detection pipeline is still an open problem. A na

ıve way

is to directly append raw RGB values to the point clouds

– since the point-pixel correspondence can be established

through projection. But since 3D points are much sparser,

in doing so we will lose the dense patterns from the im-

age domain. In light of this, more advanced ways to fuse

2D and 3D data have been proposed recently. One line of

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